Object detection has recently seen a surge of intense research interest, driven by applications in diverse fields such as video surveillance, the internet of things (IoT), and autonomous driving. While the particulars of individual use-cases may differ significantly, the basic premise of object detection remains constant: given a raster image (or sequence of images), identify the pixels corresponding to a particular object of interest, and construct polygonal boundaries or polygons to represent the object of interest from the identified pixels. However, the question of evaluating the quality of the object detections (e.g., quality of the polygonal representations of the objects) continues to present technical challenges.